Poster — Thur Eve — 18: Cherenkov Emission By High‐Energy Radiation Therapy Beams: A Characterization Study
Bibliographic record
Abstract
We investigate Cherenkov emission (CE) by radiotherapy beams via radiation dose‐versus‐CE correlation analyses, CE detection optimization by means of a spectral shift towards the near‐infrared (NIR) window of biological tissue, and comparison of CE to on‐board MV imaging. Dose‐CE correlation was investigated via simulation and experiment. A Monte Carlo (MC) CE simulator was designed using Geant4. Experimental phantoms include: water; tissue‐simulating phantom composed of water, Intralipid®, and beef blood; plastic phantom with solid water insert. The detector system comprises an optical fiber and diffraction‐grating spectrometer incorporating a front/back‐illuminated CCD. The NIR shift was carried out with CdSe/ZnS quantum dots (QDs), emitting at (650±10) nm. CE and MV images were acquired with a CMOS camera and electronic portal imaging device. MC and experimental studies indicate a strong linear dose‐CE correlation (Pearson coefficient > 0.99). CE by an 18‐MeV beam was effectively NIR‐shifted in water and a tissue‐simulating phantom, exhibiting a significant increase at 650 nm for QD depths up to 10 mm. CE images exhibited relative contrast superior to MV images by a factor of 30. Our work supports the potential for application of CE in radiotherapy online imaging for patient setup and treatment verification, since CE is intrinsic to the beam and non‐ionizing and QDs can be used to improve CE detectability, potentially yielding image quality superior to MV imaging for the case of low‐density‐variability, low‐optical‐attenuation materials (ex: breast/oropharynx). Ongoing work involves microenvironment functionalization of QDs and application of multi‐channel spectrometry for simultaneous acquisition of dosimetric and tumor oxygenation signals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".